Resolve vFunc bug by suppressing extrapolation limit

The math for the limiting linear function for the pseudo-inverse value function must not be correct, as that's what was causing the vFunc jump. It's been commented out for now.

Need to look at whether the cFunc extrapolation code is correct for ConsMarkovModel.

HARK/ConsumptionSaving/ConsMarkovModel.py

@@ -14,7 +14,7 @@
     IndShockConsumerType,
     PerfForesightConsumerType,
 )
-from HARK.distribution import MarkovProcess, Uniform, calc_expectation, expected
+from HARK.distribution import MarkovProcess, Uniform, expected
 from HARK.interpolation import (
     CubicInterp,
     LinearInterp,
@@ -473,10 +473,14 @@ def calc_vPPnext(S, a, R):
             vNvrsP_now = np.insert(
                 vNvrsP_now, 0, MPCmaxEff[i] ** (-CRRA / (1.0 - CRRA))
             )
-            MPCminNvrs = MPCminNow[i] ** (-CRRA / (1.0 - CRRA))
+            # MPCminNvrs = MPCminNow[i] ** (-CRRA / (1.0 - CRRA))
             vNvrsFuncNow = CubicInterp(
-                mNrm_temp, vNvrs_now, vNvrsP_now, MPCminNvrs * hNrmNow_i, MPCminNvrs
-            )
+                mNrm_temp,
+                vNvrs_now,
+                vNvrsP_now,
+            )  # MPCminNvrs * hNrmNow_i, MPCminNvrs)
+            # The bounding function for the pseudo-inverse value function is incorrect.
+            # TODO: Resolve this strange issue; extrapolation is suppressed for now.
 
             # "Recurve" the decurved value function and add it to the list
             vFuncNow = ValueFuncCRRA(vNvrsFuncNow, CRRA)